A quasi-periodic signal with high signal-to-noise ratio, but low temporal resolution co-exists quite often with a related signal with low signal-to-noise ratio, but high temporal resolution. The quasi-periodic nature of the signal means that conventional correlation and spectral estimation techniques cannot be easily applied. One such example is the phonocardiogram and the related plethysmogram. Acquisition of accurate, high-resolution phonocardiogram is difficult because of motion artifacts, breathing, external noise, coughing and other transient disturbances of high magnitude. The various noise sources contribute to low signal-to-noise ratios for the phonocardiogram. At the same time, a plethysmogram provides a high signal-to-noise ratio but low temporal resolution heart-beat waveform.
Research into cardiac function, cardiac imaging, and other medical research requires the identification of a temporal reference point in the cardiac cycle. An area of interest in both cardiac research and cardiac imaging demands the processing of cardiac signals and images with high temporal resolution. The ability to acquire and register these signals and images with high resolution permits researchers and clinicians to use advanced techniques for extracting signals from noise to explore the microstructure of these signals as indicators of cardiac health.
The reference point used most frequently is the peak of the R-wave exhibited by the EKG. However, the EKG is a record of the electrical excitation of the heart and not a record of its mechanical activity. It is frequently the mechanical activity that is of interest for understanding heart murmurs and other heart sounds. Thus, use of the R-wave assumes that there is a constant relationship between the peak of the R-wave and the mechanical response of the heart. Further, use of the EKG requires electrical connections to the body requiring multiple wires, resulting in increase in complexity and preparation time.
Therefore, a temporal reference point with respect to the mechanical activity of the heart is preferred.
However, to date there have not been robust ways to identify a temporal reference point with sufficient resolution and precision for high-resolution detection, processing and reconstruction of quasi-periodic signals, including but not limited to electrical, pressure, and acoustic signals.
For example, heart sound information can be extracted from the phonocardiogram both for analysis and for training clinicians. In part this is because the phonocardiograph instrument used to acquire the phonocardiogram signal has the virtue of requiring only that the clinician hold a microphone to the chest of the patient, and in part because the phonocardiogram signal provides information to the clinician that is not easily available by other means.
However, analysis of the exemplary phonocardiogram is sometimes difficult because of motion artifacts, coughing, breathing, excessive body fat, variations in the position of the phonocardiograph microphone and background noise. The sounds that the clinician wants to hear are of very low amplitude and can be difficult to discern. These sounds can be indicators of significant cardiac conditions that influence treatment and management. In other words, the phonocardiogram signal tends to have a low signal-to-noise ratio.
As discussed before, the problem of phonocardiograph signal analysis is one example of problems one would face during the analysis and reconstruction of quasi-periodic signals with low signal-to-noise ratios but high temporal resolution that is co-existent with related signals with low signal-to-noise ratio, but high temporal resolution.
Therefore, quasi-periodic signal analysis, for example cardiac sound analysis, requires a means of differentiating artifacts from real signals. This is accomplished in many cases by averaging large numbers of heart sounds together through so-called “boxcar integration.” Under the assumption that the differences between beats are due to noise, this technique averages together corresponding points in many beats, thereby building up a prototypical beat. Boxcar integration works well for periodic signals, as a temporal reference for each beat must be established with high accuracy. The quasi-periodicity of the heartbeat makes establishing this temporal reference difficult. Standard correlation techniques result in the loss of high frequency information in the signal. The techniques based on spectral estimation are also not appropriate.
Some attempts have been made to acquire and interpret quasi-periodic signals such as phonocardiogram.
U.S. Pat. No. 4,905,706 to Duff et al. describes a method and an apparatus for extracting information from acoustic heart signals and identifying coronary artery disease by recording and analyzing portion of the phonocardiogram lying between 100 to 600 Hz. An electrocardiogram is recorded and examined in order to pinpoint the diastolic window of PCG data. This window of data is subjected to autocorrelation analysis and spectral analysis, resulting in a partial correlation coefficient index and a power density index.
U.S. Pat. No. 5,109,863 to Semmlow et al., describes a method analyzing the diastolic heart sounds in order to identify a low level auditory component associated with turbulent blood flow in occluded coronary arteries. The diastolic heart sounds are modeled so that the presence of such an auditory component may be reliably indicated under high noise conditions. A method for automatically identifying and isolating a diastolic segment of a heart sound recording through a “window” placement technique is also described.
U.S. Pat. No. 5,159,932 to Zanetti et al. describes a method and apparatus for non-invasively monitoring the motion of a heart, to detect and display ischemia-induced variations in the heart's motion which indicate coronary artery disease.
U.S. Pat. No. 6,024,705 to Schlager et al. describes a computer-based automation for seismocardiographic waveform to produce a “number” for heart performance parameters, and particularly a positive-negative diagnosis of myocardial ischemia.
U.S. Pat. No. 6,572,560 to Watrous et al. teaches a method for extracting features from cardiac acoustic signals using a neural network. A wavelet is decomposed to extract time-frequency information, and identifying basic heart sounds using neural networks applied to the extracted time-frequency information.
U.S. Pat. No. 6,950,702 to Sweeney teaches a cardiac rhythm management system with a sensing circuit to sense a cardiac signal and a sensing processor to detect cardiac beats by utilizing certain morphological context of the sensed cardiac signal.
Prior art method and system, however, do not teach a method whereby two related quasi-periodic signals, one characterized by high signal-to-noise ratio, low temporal resolution and the other characterized by low signal-to-noise ratio, high temporal resolution, are used to acquire high-resolution quasi-periodic signals or to identify temporal reference points therein.
Therefore, there is a need for a novel method and system to find temporal reference points in quasi-periodic signals that indicate the same temporal point on each beat, so as to enable the use of available signal analysis methods.